| Title | Fault detection and localization based on Decision Tree and Support vector machine algorithms in electrical power transmission network |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Bouchiba, Nouha, Kaddouri, Azeddine |
| Conference Name | 2022 2nd International Conference on Advanced Electrical Engineering (ICAEE) |
| Keywords | artificial intelligence, composability, decesion tree, location awareness, machine learning, machine learning algorithms, Metrics, power transmission, Prediction algorithms, pubcrawl, resilience, Resiliency, simulation, supply vector machines, support vector machine, Support vector machines, transmission network |
| Abstract | This paper introduces an application of machine learning algorithms. In fact, support vector machine and decision tree approaches are studied and applied to compare their performances in detecting, classifying, and locating faults in the transmission network. The IEEE 14-bus transmission network is considered in this work. Besides, 13 types of faults are tested. Particularly, the one fault and the multiple fault cases are investigated and tested separately. Fault simulations are performed using the SimPowerSystems toolbox in Matlab. Basing on the accuracy score, a comparison is made between the proposed approaches while testing simple faults, on the one hand, and when complicated faults are integrated, on the other hand. Simulation results prove that the support vector machine technique can achieve an accuracy of 87% compared to the decision tree which had an accuracy of 53% in complicated cases. |
| DOI | 10.1109/ICAEE53772.2022.9961970 |
| Citation Key | bouchiba_fault_2022 |
Fault detection and localization based on Decision Tree and Support vector machine algorithms in electrical power transmission network